Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compro...Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.展开更多
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from...Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.展开更多
Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have...Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.展开更多
The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically co...The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically complex areas.However,current UAV-based detection methods face several critical limitations,including constrained deployment frequency,limited availability of annotated defect data,and the lack of mature risk assessment frameworks.To address these challenges,this study introduces a novel approach that integrates diffusion models with Large Language Models(LLMs)to generate highquality synthetic defect images tailored to railway slope scenarios.Furthermore,an improved transformerbased architecture is proposed,incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions.Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision,recall,and robustness,indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.展开更多
The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has dri...The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has driven a growing need for robust ICS security measures.Among the key defences,intrusion detection technology is critical in identifying threats to ICS networks.This paper provides an overview of the distinctive characteristics of ICS network security,highlighting standard attack methods.It then examines various intrusion detection methods,including those based on misuse detection,anomaly detection,machine learning,and specialised requirements.This paper concludes by exploring future directions for developing intrusion detection systems to advance research and ensure the continued security and reliability of ICS operations.展开更多
Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,ex...Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features,resulting in fragmented feature representations that compromise model stability and accuracy.To improve the extraction of valuable features,this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering(EDESC-IDS).Following the standard learning approach,continuous messages are encoded into two-dimensional data frames via a frame builder,which are then input into an extended convolutional autoencoder for extracting shallow features from high-dimensional data.On this basis,the dual constraints of these output features and the embedding clustering module facilitate end-to-end training of the EDESC-IDS in various attack scenarios.Extensive experimental results show that such a system exhibits significant detection performance on four types of attack datasets,including DoS,Gear,Fuzzy,and RPM,with precision,recall,and F1 scores consistently above 97.79%,while maintaining a false negative rate(FNR)and an error rate(ER)below 2.22%.展开更多
Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.Howe...Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.展开更多
The increasing adoption of Industrial Internet of Things(IIoT)systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems(IDS)to be effective.How...The increasing adoption of Industrial Internet of Things(IIoT)systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems(IDS)to be effective.However,existing datasets for IDS training often lack relevance to modern IIoT environments,limiting their applicability for research and development.To address the latter gap,this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems.As a consequence,that can be used by an IDS to detect imminent threats.Likewise,HiTar-2024 was generated using the AREZZO simulator,which replicates realistic smart manufacturing scenarios.The generated dataset includes five distinct classes:Normal,Probing,Remote to Local(R2L),User to Root(U2R),and Denial of Service(DoS).Furthermore,comprehensive experiments with popular Machine Learning(ML)models using various classifiers,including BayesNet,Logistic,IBK,Multiclass,PART,and J48 demonstrate high accuracy,precision,recall,and F1-scores,exceeding 0.99 across all ML metrics.The latter result is reached thanks to the rigorous applied process to achieve this quite good result,including data pre-processing,features extraction,fixing the class imbalance problem,and using a test option for model robustness.This comprehensive approach emphasizes meticulous dataset construction through a complete dataset generation process,a careful labelling algorithm,and a sophisticated evaluation method,providing valuable insights to reinforce IIoT system security.Finally,the HiTar-2024 dataset is compared with other similar datasets in the literature,considering several factors such as data format,feature extraction tools,number of features,attack categories,number of instances,and ML metrics.展开更多
In order to better build the neutral beam injector with negative ion source(NNBI),the pre-research on key technologies has been carried out for the comprehensive research facility for fusion technology(CRFFT).Cesium s...In order to better build the neutral beam injector with negative ion source(NNBI),the pre-research on key technologies has been carried out for the comprehensive research facility for fusion technology(CRFFT).Cesium seeding into negative-ion sources is a prerequisite to obtain the required negative hydrogen ion.The performance of ion source largely depends on the cesium conditions in the source.It is very necessary to quantitatively measure the amount of cesium in the source during the plasma on and off periods(vacuum stage).展开更多
Roaming in 5G networks enables seamless global mobility but also introduces significant security risks due to legacy protocol dependencies,uneven Security Edge Protection Proxy(SEPP)deployment,and the dynamic nature o...Roaming in 5G networks enables seamless global mobility but also introduces significant security risks due to legacy protocol dependencies,uneven Security Edge Protection Proxy(SEPP)deployment,and the dynamic nature of inter-Public Land Mobile Network(inter-PLMN)signaling.Traditional rule-based defenses are inadequate for protecting cloud-native 5G core networks,particularly as roaming expands into enterprise and Internet of Things(IoT)domains.This work addresses these challenges by designing a scalable 5G Standalone testbed,generating the first intrusion detection dataset specifically tailored to roaming threats,and proposing a deep learning based intrusion detection framework for cloud-native environments.Six deep learning models including Multilayer Perceptron(MLP),one-dimensional Convolutional Neural Network(1D CNN),Autoencoder(AE),Recurrent Neural Network(RNN),Gated Recurrent Unit(GRU),and Long Short-Term Memory(LSTM)were evaluated on the dataset using both weighted and balanced metrics to account for strong class imbalance.While all models achieved over 99%accuracy,recurrent architectures such as GRU and LSTM outperformed others in balanced accuracy and macro-level evaluation,demonstrating superior effectiveness in detecting rare but high-impact attacks.These results confirm the importance of sequence-aware Artificial Intelligence(AI)models for securing roaming scenarios,where transient and contextdependent threats are common.The proposed framework provides a foundation for intelligent,adaptive intrusion detection in 5G and offers a path toward resilient security in Beyond 5G and 6G networks.展开更多
The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-d...The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-driven intrusion detection system for Distributed Denial of Service(DDoS)attack detection.The system focuses on intrusion detection from a big data perceptive.As intelligent information processing methods,big data and artificial intelligence have been widely used in information systems.The INS system is an important information system in cyberspace.In advanced INS systems,the network architectures have become more complex.And the smart devices in INS systems collect a large scale of network data.How to improve the performance of a complex intrusion detection system with big data and artificial intelligence is a big challenge.To address the problem,we design a novel intrusion detection system(IDS)from a big data perspective.The IDS system uses tensors to represent large-scale and complex multi-source network data in a unified tensor.Then,a novel tensor decomposition(TD)method is developed to complete big data mining.The TD method seamlessly collaborates with the XGBoost(eXtreme Gradient Boosting)method to complete the intrusion detection.To verify the proposed IDS system,a series of experiments is conducted on two real network datasets.The results revealed that the proposed IDS system attained an impressive accuracy rate over 98%.Additionally,by altering the scale of the datasets,the proposed IDS system still maintains excellent detection performance,which demonstrates the proposed IDS system’s robustness.展开更多
Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusio...Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.展开更多
The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This pa...The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm(GA)with a“double auction”method.This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework.It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders.The GA functions as an intelligent search mechanism that identifies optimal combinations of bids from users and suppliers,addressing issues arising from the intricacies of cloud systems.Analyses proved that our method surpasses previous strategies,particularly in terms of price accuracy,speed,and the capacity to manage large-scale activities,critical factors for real-time cybersecurity systems,such as IDS.Our research integrates artificial intelligence-inspired evolutionary algorithms with market-driven methods to develop intelligent resource management systems that are secure,scalable,and adaptable to evolving risks,such as process innovation.展开更多
With the rapid development of computer vision technology,artificial intelligence algorithms,and high-performance computing platforms,machine vision technology has gradually shown its great potential in automated produ...With the rapid development of computer vision technology,artificial intelligence algorithms,and high-performance computing platforms,machine vision technology has gradually shown its great potential in automated production lines,especially in defect detection.Machine vision technology can be applied in many industries such as semiconductor,automobile manufacturing,aerospace,food,and drugs,which can significantly improve detection efficiency and accuracy,reduce labor costs,improve product quality,enhance market competitiveness,and provide strong support for the arrival of Industry 4.0 era.In this article,the concept,advantages,and disadvantages of machine vision and the algorithm framework of machine vision in the defect detection system are briefly described,aiming to promote the rapid development of industry and strengthen China’s industry.展开更多
The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges,particularly in safeguarding sensitive patient data and maintaining the integrity of m...The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges,particularly in safeguarding sensitive patient data and maintaining the integrity of medical services.As healthcare becomes more data-driven,cyberattacks targeting these systems continue to rise,necessitating the development of robust,domain-adapted Intrusion Detection Systems(IDS).However,current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare.To address this gap,this study introduces HCKDDCUP,a synthetic dataset modeled on the widely used KDDCUP benchmark,augmented with healthcare-relevant attributes such as patient data,treatments,and diagnoses to better simulate the unique conditions of clinical environments.This research applies standard machine learning algorithms Random Forest(RF),Decision Tree(DT),and K-Nearest Neighbors(KNN)to both the KDDCUP and HCKDDCUP datasets.The methodology includes data preprocessing,feature selection,dimensionality reduction,and comparative performance evaluation.Experimental results show that the RF model performed best,achieving 98%accuracy on KDDCUP and 99%on HCKDDCUP,highlighting its effectiveness in detecting cyber intrusions within a healthcare-specific context.This work contributes a valuable resource for future research and underscores the need for IDS development tailored to sector-specific requirements.展开更多
Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,...Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.展开更多
Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired(BVI)individuals in a limited timeframe in the event of emergencies,particularly in enclosed...Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired(BVI)individuals in a limited timeframe in the event of emergencies,particularly in enclosed areas.Fire detection becomes crucial as it directly impacts human safety and the environment.While modern technology requires precise techniques for early detection to prevent damage and loss,few research has focused on artificial intelligence(AI)-based early fire alert systems for BVI individuals in indoor settings.To prevent such fire incidents,it is crucial to identify fires accurately and promptly,and alert BVI personnel using a combination of smart glasses,deep learning(DL),and computer vision(CV).The most recent technologies require effective methods to identify fires quickly,preventing damage and physical loss.In this manuscript,an Enhanced Fire Detection System for Blind and Visually Challenged People using Artificial Intelligence with Deep Convolutional Neural Networks(EFDBVC-AIDCNN)model is presented.The EFDBVC-AIDCNN model presents an advanced fire detection system that utilizes AI to detect and classify fire hazards for BVI people effectively.Initially,image pre-processing is performed using the Gabor filter(GF)model to improve texture details and patterns specific to flames and smoke.For the feature extractor,the Swin transformer(ST)model captures fine details across multiple scales to represent fire patterns accurately.Furthermore,the Elman neural network(ENN)technique is implemented to detect fire.The improved whale optimization algorithm(IWOA)is used to efficiently tune ENN parameters,improving accuracy and robustness across varying lighting and environmental conditions to optimize performance.An extensive experimental study of the EFDBVC-AIDCNN technique is accomplished under the fire detection dataset.A short comparative analysis of the EFDBVC-AIDCNN approach portrayed a superior accuracy value of 96.60%over existing models.展开更多
Network intrusion detection systems(IDS)are a prevalent method for safeguarding network traffic against attacks.However,existing IDS primarily depend on machine learning(ML)models,which are vulnerable to evasion throu...Network intrusion detection systems(IDS)are a prevalent method for safeguarding network traffic against attacks.However,existing IDS primarily depend on machine learning(ML)models,which are vulnerable to evasion through adversarial examples.In recent years,the Wasserstein Generative Adversarial Network(WGAN),based on Wasserstein distance,has been extensively utilized to generate adversarial examples.Nevertheless,several challenges persist:(1)WGAN experiences the mode collapse problem when generating multi-category network traffic data,leading to subpar quality and insufficient diversity in the generated data;(2)Due to unstable training processes,the authenticity of the data produced by WGAN is often low.This study improves WGAN to address these issues and proposes a new adversarial sample generation algorithm called Distortion Enhanced Multi-Generator Generative Adversarial Network(DEMGAN).DEMGAN effectively evades ML-based IDS by proficiently obfuscating network traffic data samples.We assess the efficacy of our attack method against five ML-based IDS using two public datasets.The results demonstrate that our method can successfully bypass IDS,achieving average evasion rates of 97.42%and 87.51%,respectively.Furthermore,empirical findings indicate that retraining the IDS with the generated adversarial samples significantly bolsters the system’s capability to detect adversarial samples,resulting in an average recognition rate increase of 86.78%.This approach not only enhances the performance of the IDS but also strengthens the network’s resilience against potential threats,thereby optimizing network security measures.展开更多
Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,an...Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.展开更多
With the rapid advancement of mobile communication networks,key technologies such as Multi-access Edge Computing(MEC)and Network Function Virtualization(NFV)have enhanced the quality of service for 5G users but have a...With the rapid advancement of mobile communication networks,key technologies such as Multi-access Edge Computing(MEC)and Network Function Virtualization(NFV)have enhanced the quality of service for 5G users but have also significantly increased the complexity of network threats.Traditional static defense mechanisms are inadequate for addressing the dynamic and heterogeneous nature of modern attack vectors.To overcome these challenges,this paper presents a novel algorithmic framework,SD-5G,designed for high-precision intrusion detection in 5G environments.SD-5G adopts a three-stage architecture comprising traffic feature extraction,elastic representation,and adaptive classification.Specifically,an enhanced Concrete Autoencoder(CAE)is employed to reconstruct and compress high-dimensional network traffic features,producing compact and expressive representations suitable for large-scale 5G deployments.To further improve accuracy in ambiguous traffic classification,a Residual Convolutional Long Short-Term Memory model with an attention mechanism(ResCLA)is introduced,enabling multi-level modeling of spatial–temporal dependencies and effective detection of subtle anomalies.Extensive experiments on benchmark datasets—including 5G-NIDD,CIC-IDS2017,ToN-IoT,and BoT-IoT—demonstrate that SD-5G consistently achieves F1 scores exceeding 99.19%across diverse network environments,indicating strong generalization and real-time deployment capabilities.Overall,SD-5G achieves a balance between detection accuracy and deployment efficiency,offering a scalable,flexible,and effective solution for intrusion detection in 5G and next-generation networks.展开更多
文摘Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.
基金supported by the Major Science and Technology Programs in Henan Province(No.241100210100)Henan Provincial Science and Technology Research Project(No.252102211085,No.252102211105)+3 种基金Endogenous Security Cloud Network Convergence R&D Center(No.602431011PQ1)The Special Project for Research and Development in Key Areas of Guangdong Province(No.2021ZDZX1098)The Stabilization Support Program of Science,Technology and Innovation Commission of Shenzhen Municipality(No.20231128083944001)The Key scientific research projects of Henan higher education institutions(No.24A520042).
文摘Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R500)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Safeguarding modern networks from cyber intrusions has become increasingly challenging as attackers continually refine their evasion tactics.Although numerousmachine-learning-based intrusion detection systems(IDS)have been developed,their effectiveness is often constrained by high dimensionality and redundant features that degrade both accuracy and efficiency.This study introduces a hybrid feature-selection framework that integrates the exploration capability of Prairie Dog Optimization(PDO)with the exploitation behavior of Ant Colony Optimization(ACO).The proposed PDO–ACO algorithm identifies a concise yet discriminative subset of features from the NSLKDD dataset and evaluates them using a Support Vector Machine(SVM)classifier.Experimental analyses reveal that the PDO–ACO model achieves superior detection accuracy of 98%while significantly lowering false alarms and computational overhead.Further validation on the CEC2017 benchmark suite confirms the robustness and adaptability of the hybrid model across diverse optimization landscapes,positioning PDO–ACO as an efficient and scalable approach for intelligent intrusion detection.
基金supported in part by the National Natural Science Foundation of China under Grant 52432012in part by the Shanghai Science and Technology Project with 25ZR1402508。
文摘The use of Unmanned Aerial Vehicles(UAVs)for defect detection on railway slopes is becoming increasingly widespread due to their ability to capture high-resolution images over large,inaccessible,and topographically complex areas.However,current UAV-based detection methods face several critical limitations,including constrained deployment frequency,limited availability of annotated defect data,and the lack of mature risk assessment frameworks.To address these challenges,this study introduces a novel approach that integrates diffusion models with Large Language Models(LLMs)to generate highquality synthetic defect images tailored to railway slope scenarios.Furthermore,an improved transformerbased architecture is proposed,incorporating attention mechanisms and LLM-guided diffusion-generated imagery to enhance defect recognition performance under complex environmental conditions.Experimental evaluations conducted on a dataset of 300 field-collected images from high-risk railway slopes demonstrate that the proposed method significantly outperforms existing baselines in terms of precision,recall,and robustness,indicating strong applicability for real-world railway infrastructure monitoring and disaster prevention.
文摘The increasing interconnection of modern industrial control systems(ICSs)with the Internet has enhanced operational efficiency,but alsomade these systemsmore vulnerable to cyberattacks.This heightened exposure has driven a growing need for robust ICS security measures.Among the key defences,intrusion detection technology is critical in identifying threats to ICS networks.This paper provides an overview of the distinctive characteristics of ICS network security,highlighting standard attack methods.It then examines various intrusion detection methods,including those based on misuse detection,anomaly detection,machine learning,and specialised requirements.This paper concludes by exploring future directions for developing intrusion detection systems to advance research and ensure the continued security and reliability of ICS operations.
基金supported by the National Natural Science Foundation of China(Grant No.62172292).
文摘Anomaly detection is a vibrant research direction in controller area networks,which provides the fundamental real-time data transmission underpinning in-vehicle data interaction for the internet of vehicles.However,existing unsupervised learning methods suffer from insufficient temporal and spatial constraints on shallow features,resulting in fragmented feature representations that compromise model stability and accuracy.To improve the extraction of valuable features,this paper investigates the influence of clustering constraints on shallow feature convergence paths at the model level and further proposes an end-to-end intrusion detection system based on efficient deep embedded subspace clustering(EDESC-IDS).Following the standard learning approach,continuous messages are encoded into two-dimensional data frames via a frame builder,which are then input into an extended convolutional autoencoder for extracting shallow features from high-dimensional data.On this basis,the dual constraints of these output features and the embedding clustering module facilitate end-to-end training of the EDESC-IDS in various attack scenarios.Extensive experimental results show that such a system exhibits significant detection performance on four types of attack datasets,including DoS,Gear,Fuzzy,and RPM,with precision,recall,and F1 scores consistently above 97.79%,while maintaining a false negative rate(FNR)and an error rate(ER)below 2.22%.
基金the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300100)the National Key Research and Development Program of China(Grant Nos.2023YFB3809600 and 2023YFC3007801)+1 种基金the National Natural Science Foundation of China(Grant Nos.62301543 and U24A20320)the Shanghai Sailing Program(Grant No.21YF1455700).
文摘Conventional superconducting nanowire single-photon detectors(SNSPDs)have been typically limited in their applications due to their size,weight,and power consumption,which confine their use to laboratory settings.However,with the rapid development of remote imaging,sensing technologies,and long-range quantum communication with fewer topographical constraints,the demand for high-efficiency single-photon detectors integrated with avionic platforms is rapidly growing.We herein designed and manufactured the first drone-based SNSPD system with a system detection efficiency(SDE)as high as 91.8%.This drone-based system incorporates high-performance NbTiN SNSPDs,a self-developed miniature liquid helium dewar,and custom-built integrated electrical setups,making it capable of being launched in complex topographical conditions.Such a drone-based SNSPD system may open the use of SNSPDs for applications that demand high SDE in complex environments.
文摘The increasing adoption of Industrial Internet of Things(IIoT)systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems(IDS)to be effective.However,existing datasets for IDS training often lack relevance to modern IIoT environments,limiting their applicability for research and development.To address the latter gap,this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems.As a consequence,that can be used by an IDS to detect imminent threats.Likewise,HiTar-2024 was generated using the AREZZO simulator,which replicates realistic smart manufacturing scenarios.The generated dataset includes five distinct classes:Normal,Probing,Remote to Local(R2L),User to Root(U2R),and Denial of Service(DoS).Furthermore,comprehensive experiments with popular Machine Learning(ML)models using various classifiers,including BayesNet,Logistic,IBK,Multiclass,PART,and J48 demonstrate high accuracy,precision,recall,and F1-scores,exceeding 0.99 across all ML metrics.The latter result is reached thanks to the rigorous applied process to achieve this quite good result,including data pre-processing,features extraction,fixing the class imbalance problem,and using a test option for model robustness.This comprehensive approach emphasizes meticulous dataset construction through a complete dataset generation process,a careful labelling algorithm,and a sophisticated evaluation method,providing valuable insights to reinforce IIoT system security.Finally,the HiTar-2024 dataset is compared with other similar datasets in the literature,considering several factors such as data format,feature extraction tools,number of features,attack categories,number of instances,and ML metrics.
基金supported by the HFIPS Director’s Fund(Nos.YZJJQY202204 and 2021YZGH02)the Comprehensive Research Facility for Fusion Technology Program of China(No.2018-000052-73-01-001228)+1 种基金the Natural Science Foundation of Anhui Province(No.2208085MA19)the National Key R&D Program of China(Nos.2017YFE300103 and 2017YFE300503)。
文摘In order to better build the neutral beam injector with negative ion source(NNBI),the pre-research on key technologies has been carried out for the comprehensive research facility for fusion technology(CRFFT).Cesium seeding into negative-ion sources is a prerequisite to obtain the required negative hydrogen ion.The performance of ion source largely depends on the cesium conditions in the source.It is very necessary to quantitatively measure the amount of cesium in the source during the plasma on and off periods(vacuum stage).
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00441484,Development of Open Roaming Technology for Private 5G Network)。
文摘Roaming in 5G networks enables seamless global mobility but also introduces significant security risks due to legacy protocol dependencies,uneven Security Edge Protection Proxy(SEPP)deployment,and the dynamic nature of inter-Public Land Mobile Network(inter-PLMN)signaling.Traditional rule-based defenses are inadequate for protecting cloud-native 5G core networks,particularly as roaming expands into enterprise and Internet of Things(IoT)domains.This work addresses these challenges by designing a scalable 5G Standalone testbed,generating the first intrusion detection dataset specifically tailored to roaming threats,and proposing a deep learning based intrusion detection framework for cloud-native environments.Six deep learning models including Multilayer Perceptron(MLP),one-dimensional Convolutional Neural Network(1D CNN),Autoencoder(AE),Recurrent Neural Network(RNN),Gated Recurrent Unit(GRU),and Long Short-Term Memory(LSTM)were evaluated on the dataset using both weighted and balanced metrics to account for strong class imbalance.While all models achieved over 99%accuracy,recurrent architectures such as GRU and LSTM outperformed others in balanced accuracy and macro-level evaluation,demonstrating superior effectiveness in detecting rare but high-impact attacks.These results confirm the importance of sequence-aware Artificial Intelligence(AI)models for securing roaming scenarios,where transient and contextdependent threats are common.The proposed framework provides a foundation for intelligent,adaptive intrusion detection in 5G and offers a path toward resilient security in Beyond 5G and 6G networks.
基金supported in part by the National Nature Science Foundation of China under Project 62166047in part by the Yunnan International Joint Laboratory of Natural Rubber Intelligent Monitor and Digital Applications under Grant 202403AP140001in part by the Xingdian Talent Support Program under Grant YNWR-QNBJ-2019-270.
文摘The era of big data brings new challenges for information network systems(INS),simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems.In this work,we propose a data-driven intrusion detection system for Distributed Denial of Service(DDoS)attack detection.The system focuses on intrusion detection from a big data perceptive.As intelligent information processing methods,big data and artificial intelligence have been widely used in information systems.The INS system is an important information system in cyberspace.In advanced INS systems,the network architectures have become more complex.And the smart devices in INS systems collect a large scale of network data.How to improve the performance of a complex intrusion detection system with big data and artificial intelligence is a big challenge.To address the problem,we design a novel intrusion detection system(IDS)from a big data perspective.The IDS system uses tensors to represent large-scale and complex multi-source network data in a unified tensor.Then,a novel tensor decomposition(TD)method is developed to complete big data mining.The TD method seamlessly collaborates with the XGBoost(eXtreme Gradient Boosting)method to complete the intrusion detection.To verify the proposed IDS system,a series of experiments is conducted on two real network datasets.The results revealed that the proposed IDS system attained an impressive accuracy rate over 98%.Additionally,by altering the scale of the datasets,the proposed IDS system still maintains excellent detection performance,which demonstrates the proposed IDS system’s robustness.
基金supported in part by the National Natural Science Foundation of China(No.62001333)the Scientific Research Project of Education Department of Hubei Province(No.D20221702).
文摘Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.
文摘The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm(GA)with a“double auction”method.This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework.It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders.The GA functions as an intelligent search mechanism that identifies optimal combinations of bids from users and suppliers,addressing issues arising from the intricacies of cloud systems.Analyses proved that our method surpasses previous strategies,particularly in terms of price accuracy,speed,and the capacity to manage large-scale activities,critical factors for real-time cybersecurity systems,such as IDS.Our research integrates artificial intelligence-inspired evolutionary algorithms with market-driven methods to develop intelligent resource management systems that are secure,scalable,and adaptable to evolving risks,such as process innovation.
文摘With the rapid development of computer vision technology,artificial intelligence algorithms,and high-performance computing platforms,machine vision technology has gradually shown its great potential in automated production lines,especially in defect detection.Machine vision technology can be applied in many industries such as semiconductor,automobile manufacturing,aerospace,food,and drugs,which can significantly improve detection efficiency and accuracy,reduce labor costs,improve product quality,enhance market competitiveness,and provide strong support for the arrival of Industry 4.0 era.In this article,the concept,advantages,and disadvantages of machine vision and the algorithm framework of machine vision in the defect detection system are briefly described,aiming to promote the rapid development of industry and strengthen China’s industry.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2501).
文摘The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges,particularly in safeguarding sensitive patient data and maintaining the integrity of medical services.As healthcare becomes more data-driven,cyberattacks targeting these systems continue to rise,necessitating the development of robust,domain-adapted Intrusion Detection Systems(IDS).However,current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare.To address this gap,this study introduces HCKDDCUP,a synthetic dataset modeled on the widely used KDDCUP benchmark,augmented with healthcare-relevant attributes such as patient data,treatments,and diagnoses to better simulate the unique conditions of clinical environments.This research applies standard machine learning algorithms Random Forest(RF),Decision Tree(DT),and K-Nearest Neighbors(KNN)to both the KDDCUP and HCKDDCUP datasets.The methodology includes data preprocessing,feature selection,dimensionality reduction,and comparative performance evaluation.Experimental results show that the RF model performed best,achieving 98%accuracy on KDDCUP and 99%on HCKDDCUP,highlighting its effectiveness in detecting cyber intrusions within a healthcare-specific context.This work contributes a valuable resource for future research and underscores the need for IDS development tailored to sector-specific requirements.
基金FEDER/Ministry of Science and Innovation-State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033the Basque Government,IT1726-22+2 种基金by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Governmentpartially supported by the Italian MIUR,PRIN 2020 Project“COMMON-WEARS”,N.2020HCWWLP,CUP:H23C22000230005co-funding from Next Generation EU,in the context of the National Recovery and Resilience Plan,through the Italian MUR,PRIN 2022 Project”COCOWEARS”(A framework for COntinuum COmputing WEARable Systems),N.2022T2XNJE,CUP:H53D23003640006.
文摘Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
基金the King Salman Centre for Disability Research for funding this work through Research Group No.KSRG-2024-068。
文摘Earlier notification and fire detection methods provide safety information and fire prevention to blind and visually impaired(BVI)individuals in a limited timeframe in the event of emergencies,particularly in enclosed areas.Fire detection becomes crucial as it directly impacts human safety and the environment.While modern technology requires precise techniques for early detection to prevent damage and loss,few research has focused on artificial intelligence(AI)-based early fire alert systems for BVI individuals in indoor settings.To prevent such fire incidents,it is crucial to identify fires accurately and promptly,and alert BVI personnel using a combination of smart glasses,deep learning(DL),and computer vision(CV).The most recent technologies require effective methods to identify fires quickly,preventing damage and physical loss.In this manuscript,an Enhanced Fire Detection System for Blind and Visually Challenged People using Artificial Intelligence with Deep Convolutional Neural Networks(EFDBVC-AIDCNN)model is presented.The EFDBVC-AIDCNN model presents an advanced fire detection system that utilizes AI to detect and classify fire hazards for BVI people effectively.Initially,image pre-processing is performed using the Gabor filter(GF)model to improve texture details and patterns specific to flames and smoke.For the feature extractor,the Swin transformer(ST)model captures fine details across multiple scales to represent fire patterns accurately.Furthermore,the Elman neural network(ENN)technique is implemented to detect fire.The improved whale optimization algorithm(IWOA)is used to efficiently tune ENN parameters,improving accuracy and robustness across varying lighting and environmental conditions to optimize performance.An extensive experimental study of the EFDBVC-AIDCNN technique is accomplished under the fire detection dataset.A short comparative analysis of the EFDBVC-AIDCNN approach portrayed a superior accuracy value of 96.60%over existing models.
基金supported by the National Defense Basic Scientific Research Programof China under grant No.JCKY2023602C026.
文摘Network intrusion detection systems(IDS)are a prevalent method for safeguarding network traffic against attacks.However,existing IDS primarily depend on machine learning(ML)models,which are vulnerable to evasion through adversarial examples.In recent years,the Wasserstein Generative Adversarial Network(WGAN),based on Wasserstein distance,has been extensively utilized to generate adversarial examples.Nevertheless,several challenges persist:(1)WGAN experiences the mode collapse problem when generating multi-category network traffic data,leading to subpar quality and insufficient diversity in the generated data;(2)Due to unstable training processes,the authenticity of the data produced by WGAN is often low.This study improves WGAN to address these issues and proposes a new adversarial sample generation algorithm called Distortion Enhanced Multi-Generator Generative Adversarial Network(DEMGAN).DEMGAN effectively evades ML-based IDS by proficiently obfuscating network traffic data samples.We assess the efficacy of our attack method against five ML-based IDS using two public datasets.The results demonstrate that our method can successfully bypass IDS,achieving average evasion rates of 97.42%and 87.51%,respectively.Furthermore,empirical findings indicate that retraining the IDS with the generated adversarial samples significantly bolsters the system’s capability to detect adversarial samples,resulting in an average recognition rate increase of 86.78%.This approach not only enhances the performance of the IDS but also strengthens the network’s resilience against potential threats,thereby optimizing network security measures.
基金supported by the Natural Science Foundation of China,Grant No.62103052.
文摘Drone swarm systems,equipped with photoelectric imaging and intelligent target perception,are essential for reconnaissance and strike missions in complex and high-risk environments.They excel in information sharing,anti-jamming capabilities,and combat performance,making them critical for future warfare.However,varied perspectives in collaborative combat scenarios pose challenges to object detection,hindering traditional detection algorithms and reducing accuracy.Limited angle-prior data and sparse samples further complicate detection.This paper presents the Multi-View Collaborative Detection System,which tackles the challenges of multi-view object detection in collaborative combat scenarios.The system is designed to enhance multi-view image generation and detection algorithms,thereby improving the accuracy and efficiency of object detection across varying perspectives.First,an observation model for three-dimensional targets through line-of-sight angle transformation is constructed,and a multi-view image generation algorithm based on the Pix2Pix network is designed.For object detection,YOLOX is utilized,and a deep feature extraction network,BA-RepCSPDarknet,is developed to address challenges related to small target scale and feature extraction challenges.Additionally,a feature fusion network NS-PAFPN is developed to mitigate the issue of deep feature map information loss in UAV images.A visual attention module(BAM)is employed to manage appearance differences under varying angles,while a feature mapping module(DFM)prevents fine-grained feature loss.These advancements lead to the development of BA-YOLOX,a multi-view object detection network model suitable for drone platforms,enhancing accuracy and effectively targeting small objects.
文摘With the rapid advancement of mobile communication networks,key technologies such as Multi-access Edge Computing(MEC)and Network Function Virtualization(NFV)have enhanced the quality of service for 5G users but have also significantly increased the complexity of network threats.Traditional static defense mechanisms are inadequate for addressing the dynamic and heterogeneous nature of modern attack vectors.To overcome these challenges,this paper presents a novel algorithmic framework,SD-5G,designed for high-precision intrusion detection in 5G environments.SD-5G adopts a three-stage architecture comprising traffic feature extraction,elastic representation,and adaptive classification.Specifically,an enhanced Concrete Autoencoder(CAE)is employed to reconstruct and compress high-dimensional network traffic features,producing compact and expressive representations suitable for large-scale 5G deployments.To further improve accuracy in ambiguous traffic classification,a Residual Convolutional Long Short-Term Memory model with an attention mechanism(ResCLA)is introduced,enabling multi-level modeling of spatial–temporal dependencies and effective detection of subtle anomalies.Extensive experiments on benchmark datasets—including 5G-NIDD,CIC-IDS2017,ToN-IoT,and BoT-IoT—demonstrate that SD-5G consistently achieves F1 scores exceeding 99.19%across diverse network environments,indicating strong generalization and real-time deployment capabilities.Overall,SD-5G achieves a balance between detection accuracy and deployment efficiency,offering a scalable,flexible,and effective solution for intrusion detection in 5G and next-generation networks.
